code: def _get_maybe_extend_periods(
      df: pd.DataFrame,
      n_forecasts: int,
      max_lags: int,
      config_regressors: Optional[OrderedDictType[str, Regressor]],
  ) -> int:
      """
      Determine the number of periods to extend the input DataFrame based on the
      number of forecasts, maximum lags, and regressor configuration.
  
  
      Parameters
      ----------
      df : pd.DataFrame
          Input DataFrame with a single ID column.
      n_forecasts : int
          Number of steps ahead of prediction time step to forecast.
      max_lags : int
          Maximum number of lags to consider.
      config_regressors : OrderedDictType[str, Regressor]
          Configuration of regressors. If None, the function may extend the
          DataFrame based on `n_forecasts` and `max_lags`.
  
      Returns
      -------
      int
          Number of periods to extend the input DataFrame.
  
      Raises
      ------
      AssertionError
          If the input DataFrame contains more than one unique ID.
  
      Notes
      -----
      The function assumes that the input DataFrame contains columns 'ID' and 'y'.
      """
      # Receives df with single ID column
      assert len(df["ID"].unique()) == 1
      periods_add = 0
      nan_at_end = 0
      while (
          len(df) > nan_at_end
          and df["y"].isnull().iloc[-(1 + nan_at_end)]
      ):
          nan_at_end += 1
      if max_lags > 0:
          if config_regressors is None:
              # if dataframe has already been extended into future,
              # don't extend beyond n_forecasts.
              periods_add = max(0, n_forecasts - nan_at_end)
          else:
              # can not extend as we lack future regressor values.
              periods_add = 0
      return periods_add
  
file_path: neural_prophet/neuralprophet/data/split.py
imports: import logging
  from typing import Optional
  from typing import OrderedDict
  from typing import Tuple
  import pandas
  from neuralprophet import df_utils
  from neuralprophet.configure import ConfigEvents, Regressor
  from neuralprophet.data.process import _check_dataframe
label_desc: Use image node_id 2 for calling a global function with example usage: _get_maybe_extend_periods(df, n_forecasts, max_lags, config_regressors) and returns: periods_add